| import os |
| from typing import Any |
|
|
| class LLMRegistry: |
| _models = {} |
| |
| @classmethod |
| def register(cls, name): |
| def decorator(model_class): |
| cls._models[name] = model_class |
| return model_class |
| return decorator |
| |
| @classmethod |
| def get_model(cls, name): |
| if name not in cls._models: |
| raise ValueError(f"Model {name} not found in registry") |
| return cls._models[name] |
|
|
| @LLMRegistry.register("Qwen2-VL") |
| class Qwen2VL: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| if os.environ.get("use_vllm", "True") == "True": |
| from models.Qwen2_VL.Qwen2_VL_vllm import Qwen2VL |
| else: |
| from models.Qwen2_VL.Qwen2_VL_hf import Qwen2VL |
| return Qwen2VL(model_path, args) |
|
|
| @LLMRegistry.register("Qwen2.5-VL") |
| class Qwen2_5_VL: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| if os.environ.get("use_vllm", "True") == "True": |
| from models.Qwen2_5_VL.Qwen2_5_VL_vllm import Qwen2_5_VL |
| else: |
| from models.Qwen2_5_VL.Qwen2_5_VL_hf import Qwen2_5_VL |
| return Qwen2_5_VL(model_path, args) |
|
|
| @LLMRegistry.register("BiMediX2") |
| class BiMediX2: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| from models.BiMediX2.BiMediX2_hf import BiMediX2 |
| return BiMediX2(model_path, args) |
|
|
| @LLMRegistry.register("LLava_Med") |
| class LLavaMed: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| if os.environ.get("use_vllm", "True") == "True": |
| from models.LLava_Med.LLava_Med_vllm import LLavaMed |
| else: |
| from models.LLava_Med.LLava_Med_hf import LLavaMed |
| return LLavaMed(model_path, args) |
|
|
| @LLMRegistry.register("Huatuo") |
| class HuatuoGPT: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| if os.environ.get("use_vllm", "True") == "True": |
| from models.HuatuoGPT.HuatuoGPT_vllm import HuatuoGPT |
| else: |
| from models.HuatuoGPT.HuatuoGPT_hf import HuatuoGPT |
| return HuatuoGPT(model_path, args) |
|
|
| @LLMRegistry.register("InternVL") |
| class InternVL: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| if os.environ.get("use_vllm", "True") == "True": |
| from models.InternVL.InternVL_vllm import InternVL |
| else: |
| from models.InternVL.InternVL_hf import InternVL |
| return InternVL(model_path, args) |
|
|
| @LLMRegistry.register("Llama-3.2") |
| class LlamaVision: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| from models.Llama_3.Llama_3_2_vision_instruct_vllm import LlamaVision |
| return LlamaVision(model_path, args) |
|
|
| @LLMRegistry.register("LLava") |
| class Llava: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| if os.environ.get("use_vllm", "True") == "True": |
| from models.LLava.LLava_vllm import Llava |
| else: |
| from models.LLava.LLava_hf import Llava |
| return Llava(model_path, args) |
|
|
|
|
| @LLMRegistry.register("Janus") |
| class Janus: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| from models.Janus.Janus import Janus |
| return Janus(model_path, args) |
|
|
|
|
| @LLMRegistry.register("HealthGPT") |
| class HealthGPT: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| if "phi-4" in model_path: |
| from models.HealthGPT.HealthGPT_phi import HealthGPT |
| else: |
| from models.HealthGPT.HealthGPT import HealthGPT |
| return HealthGPT(model_path, args) |
|
|
| @LLMRegistry.register("BiomedGPT") |
| class BiomedGPT: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| from models.BiomedGPT.BiomedGPT import BiomedGPT |
| return BiomedGPT(model_path, args) |
|
|
| @LLMRegistry.register("TestModel") |
| class TestModel: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| from models.TestModel.TestModel import TestModel |
| return TestModel(model_path) |
|
|
| @LLMRegistry.register("Vllm_Text") |
| class VllmText: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| preprocessor_config_path = os.path.join(model_path, "preprocessor_config.json") |
| if os.path.exists(preprocessor_config_path): |
| from models.vllm_text.vllm_processor import Vllm_Text |
| else: |
| from models.vllm_text.vllm_tokenizer import Vllm_Text |
| return Vllm_Text(model_path, args) |
|
|
| @LLMRegistry.register("MedGemma") |
| class MedGemma: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| if os.environ.get("use_vllm", "True") == "True": |
| from models.MedGemma.MedGemma_vllm import MedGemma |
| else: |
| from models.MedGemma.MedGemma_hf import MedGemma |
| return MedGemma(model_path, args) |
|
|
| @LLMRegistry.register("Med_Flamingo") |
| class MedFlamingo: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| from models.Med_Flamingo.Med_Flamingo_hf import Med_Flamingo |
| return Med_Flamingo(model_path, args) |
| |
| @LLMRegistry.register("GPT") |
| class GPT: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| from models.GPT.GPT import openai_llm |
| return openai_llm(model_path, args) |
|
|
| @LLMRegistry.register("MedDr") |
| class MedDr: |
| def __new__(cls, model_path: str, args: Any) -> Any: |
| from models.MedDr.MedDr import MedDr |
| return MedDr(model_path, args) |
|
|
| def init_llm(args): |
| try: |
| model_class = LLMRegistry.get_model(args.model_name) |
| return model_class(args.model_path, args) |
| except ValueError as e: |
| raise ValueError(f"{args.model_name} not supported") from e |
|
|
|
|
